Degree Type

Creative Component

Semester of Graduation

Summer 2021

Department

Statistics

First Major Professor

Peng Liu

Degree(s)

Master of Science (MS)

Major(s)

Statistics

Abstract

Bayesian hierarchical generalized linear models are intuitively appealing for applications to gene sequencing data. However, they can be computationally costly to fit in high-dimensional settings using standard Markov-chain Monte Carlo methods. Here we explore the use of variational inference techniques to approximate the posterior of Bayesian hierarchical GLMMs for detecting differential expression in RNA-Seq data or differential translational efficiency in Ribo-Seq data. We find that in simulation studies the variational approach is comparable to two common methods for detecting differential expression, and that the variational posterior is close to the Markov-chain Monte Carlo posterior.

Copyright Owner

Walker, David

File Format

PDF

Embargo Period (admin only)

7-16-2021

1

Included in

Biostatistics Commons

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